Fourier transform based spatial outlier mining

Authors:
Faraz Rasheed;Peter Peng;Reda Alhajj;Jon Rokne
Affiliations:
Dept. of Computer Science, University of Calgary, Calgary, Alberta, Canada;Dept. of Computer Science, University of Calgary, Calgary, Alberta, Canada;Dept. of Computer Science, University of Calgary, Calgary, Alberta, Canada and Dept. of Computer Science, Global University, Beirut, Lebanon;Dept. of Computer Science, University of Calgary, Calgary, Alberta, Canada
Venue:
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Year:
2009

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Abstract

Outlier detection is an important problem in spatial analysis which involves finding a region of spatial locations with features significantly different from the rest of the population. In this paper, we used fast fourier transform to highlight the areas with high frequency change. The spatial points identified by the fourier transform are then reconfirmed with Z-value test and outlier regions are identified. We performed several experiments to highlight the accuracy and efficiency of the approach and compared it with some other existing approaches.